Laws Archives - gettectonic.com
copilots and agentic ai

Transforming Industries and Redefining Workflows

The Rise of Agentic AI: Transforming Industries and Redefining Workflows Artificial Intelligence (AI) is evolving faster than we anticipated. No longer limited to predicting outcomes or generating content, AI systems are now capable of handling complex tasks and making autonomous decisions. This new era—driven by Agentic AI—is set to redefine the workplace and transform industries. From Prediction to Autonomy: The Three Waves of AI To understand where we’re headed, it’s important to see how far AI has come. Arun Parameswaran, SVP & MD of Salesforce India, describes it as a fundamental shift: “What has changed with agents is their ability to handle complex reasoning… and, most importantly, to take action.” Unlike previous AI models that recommend or predict, Agentic AI executes tasks, reshaping customer experiences and operational workflows. Agentic AI in Action: Industry Applications At a recent Mint x Salesforce India deep-dive event on AI, industry leaders explored how Agentic AI is driving transformation across sectors. The panel featured: Here’s how Agentic AI is already making an impact: 1. Revolutionizing Customer Support Traditional chatbots have limited capabilities. Agentic AI, however, understands urgency and context. 2. Accelerating Business Decisions In finance and supply chain management, AI agents analyze vast amounts of data and execute decisions autonomously. 3. Transforming Travel & Aviation Airlines are leveraging AI to optimize booking systems, reduce costs, and enhance efficiency. 4. Automating Wealth Management AI agents in financial services monitor markets, adjust strategies, and offer personalized investment recommendations in real time. The Risks & Responsibilities of Agentic AI With great autonomy comes great responsibility. The potential of Agentic AI is vast—but so are the challenges: The Future of Work: AI as a Partner, Not a Replacement Despite concerns about job displacement, AI is more likely to reshape rather than replace roles. What Are AI Agents? AI agents go beyond traditional models like ChatGPT or Gemini. They are proactive, self-learning systems that: They fall into two categories: “AI agents don’t just wait for commands; they anticipate needs and act,” says Dr. Tomer Simon, Chief Scientist at Microsoft Research Israel. AI Agents in the Workplace: A Shift in Roles AI agents streamline processes, but they don’t eliminate the need for human oversight. Salesforce’s Agentforce is a prime example: “Companies need to integrate AI, not fear it. Those who fail to adopt AI tools risk drowning in tasks AI can handle,” warns Dr. Omri Allouche, Chief Scientist at Gong. The Road Ahead: AI-Driven Business Growth Agentic AI is not about replacing people—it’s about empowering them. As organizations re-evaluate workflows and embrace AI collaboration, the companies that act early will gain a competitive edge in efficiency and innovation. Final Thought The AI revolution is here, and Agentic AI is at its forefront. The key question isn’t whether AI will transform industries—it’s how organizations will adapt and thrive in this new era. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
computer hackers in a genai desert

How Hackers Exploit GenAI

Hackers are increasingly leveraging generative AI (GenAI) to execute sophisticated cyberattacks, with real-world incidents highlighting its growing role in cybercrime. In early 2024, fraudsters used a deepfake of a multinational firm’s CFO to trick a finance employee into transferring $25 million—a stark example of how GenAI is reshaping cyber threats. Experts warn this is just the beginning. Here’s how cybercriminals are using GenAI to their advantage: 1. Crafting Advanced Phishing & Social Engineering Attacks GenAI-powered tools like ChatGPT enable hackers to generate professional-grade phishing emails that closely mimic corporate communications. These emails, now nearly flawless in grammar and formatting, are far more convincing to targets. Additionally, GenAI can: 2. Writing & Enhancing Malicious Code Just as developers use GenAI to accelerate coding, cybercriminals use it to: This automation fuels a rise in zero-day attacks, where vulnerabilities are exploited before developers can patch them. 3. Identifying Vulnerabilities at Scale GenAI accelerates the discovery of security weaknesses by: With GenAI, cybercriminals can scale and refine their tactics faster than ever. 4. Automating Target Research & Attack Planning Hackers use GenAI to: While mainstream AI tools have built-in safeguards, threat actors find ways to bypass them, using alternative AI models or dark web resources. 5. Lowering the Barrier to Cybercrime GenAI democratizes cyberattacks by: This increased accessibility means more people—beyond seasoned cybercriminals—can launch effective cyberattacks. The Hidden Risk: AI-Powered Coding in Enterprises The security risk of GenAI isn’t limited to adversarial use. Businesses adopting AI-powered coding tools may unintentionally introduce vulnerabilities into their systems. Joseph Nwankpa, director of cybersecurity initiatives at Miami University’s Farmer School of Business, warns: The Takeaway While GenAI offers groundbreaking advancements, it also amplifies cyber threats. Organizations must remain vigilant—investing in AI security measures, strengthening human oversight, and educating employees to counter AI-powered attacks. The race between AI-driven innovation and cybercrime is just getting started. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
Apple's Privacy Changes: A Call for Email Marketing Innovation

Liar Liar Apple on Fire

Apple Developing Update After AI System Generates Inaccurate News Summaries Apple is working on a software update to address inaccuracies generated by its Apple Intelligence system after multiple instances of false news summaries were reported. The BBC first alerted Apple in mid-December to significant errors in the system, including a fabricated summary that falsely attributed a statement to BBC News. The summary suggested Luigi Mangione, accused of killing United Healthcare CEO Brian Thompson, had shot himself, a claim entirely unsubstantiated. Other publishers, such as ProPublica, also raised concerns about Apple Intelligence producing misleading summaries. While Apple did not respond immediately to the BBC’s December report, it issued a statement after pressure mounted from groups like the National Union of Journalists and Reporters Without Borders, both of which called for the removal of Apple Intelligence. Apple assured stakeholders it is working to refine the technology. A Widespread AI Issue: Hallucinations Apple joins the ranks of other AI vendors struggling with generative AI hallucinations—instances where AI produces false or misleading information. In October 2024, Perplexity AI faced a lawsuit from Dow Jones & Co. and the New York Post over fabricated news content attributed to their publications. Similarly, Google had to improve its AI summaries after providing users with inaccurate information. On January 16, Apple temporarily disabled AI-generated summaries for news apps on iPhone, iPad, and Mac devices. The Core Problem: AI Hallucination Chirag Shah, a professor of Information Science at the University of Washington, emphasized that hallucination is inherent to the way large language models (LLMs) function. “The nature of AI models is to generate, synthesize, and summarize, which makes them prone to mistakes,” Shah explained. “This isn’t something you can debug easily—it’s intrinsic to how LLMs operate.” While Apple plans to introduce an update that clearly labels summaries as AI-generated, Shah believes this measure falls short. “Most people don’t understand how these headlines or summaries are created. The responsible approach is to pause the technology until it’s better understood and mitigation strategies are in place,” he said. Legal and Brand Implications for Apple The hallucinated summaries pose significant reputational and legal risks for Apple, according to Michael Bennett, an AI adviser at Northeastern University. Before launching Apple Intelligence, the company was perceived as lagging in the AI race. The release of this system was intended to position Apple as a leader. Instead, the inaccuracies have damaged its credibility. “This type of hallucinated summarization is both an embarrassment and a serious legal liability,” Bennett said. “These errors could form the basis for defamation claims, as Apple Intelligence misattributes false information to reputable news sources.” Bennett criticized Apple’s seemingly minimal response. “It’s surprising how casual Apple’s reaction has been. This is a major issue for their brand and could expose them to significant legal consequences,” he added. Opportunity for Publishers The incident highlights the need for publishers to protect their interests when partnering with AI vendors like Apple and Google. Publishers should demand stronger safeguards to prevent false attributions and negotiate new contractual clauses to minimize brand risk. “This is an opportunity for publishers to lead the charge, pushing AI companies to refine their models or stop attributing false summaries to news sources,” Bennett said. He suggested legal action as a potential recourse if vendors fail to address these issues. Potential Regulatory Action The Federal Trade Commission (FTC) may also scrutinize the issue, as consumers paying for products like iPhones with AI capabilities could argue they are not receiving the promised service. However, Bennett believes Apple will likely act to resolve the problem before regulatory involvement becomes necessary. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More

Digital Marketing for Casinos

Unveiling the Casino Experience: Harnessing the Power of Digital Marketing In the exciting world of casinos—where entertainment meets sophistication—the influence of digital marketing is undeniable. A strategic approach is packed with actionable insights designed to boost online presence, engage audiences, and drive sustained success. Discover how to craft compelling content, wield social media’s dynamic power, utilize a customer relationship platform, and optimize visibility to ensure your casino stands out in an increasingly competitive digital arena. Tectonic has a successful, winning track record in Salesforce implementation for casinos. Whether you’re promoting exclusive guest events, captivating diverse demographics, or showcasing the unique experience of your casino, this insight equips you to master the digital space with Salesforce. With these strategies, casinos can create a ever-growing online presence that not only strengthens bonds with existing patrons but also entices new guests. Generating New and Repeat Guest Traffic with Salesforce Key Takeaways Why Digital Marketing is a Critical Component for Casinos Digital marketing serves as a critical driver of customer engagement, loyalty, and revenue growth in the casino industry. Strategies such as SEO, email marketing, and social media engagement empower casinos to connect with target audiences and continuously refine their efforts to remain competitive. In today’s crowded and competitive gaming world, leveraging data-driven marketing offers the competitive edge needed to captivate and retain customers. Winning Strategies for Casino Marketing 1. Search Engine Optimization (SEO):Ensure your casino is easy to find with these tactics: 2. Pay-Per-Click Advertising (PPC):Drive traffic with targeted PPC campaigns by: 3. Social Media Marketing:Create buzz with engaging social media campaigns: 4. Email Marketing:Maintain direct communication with: 5. Salesforce 360 Degree Guest View:Maintain personalized communication with: Reaching the Right Audience with Precision Audience Segmentation:Segmenting your audience by behavior, demographics, and preferences ensures more effective marketing. Navigating Legal and Ethical Challenges in Casino Marketing Compliance is essential in maintaining trust and navigating complex regulations. Measuring Success: Metrics and Optimization Key Metrics to Monitor: Campaign Optimization: Addressing Industry Challenges with Marketing 1. Rising Competition:Stand out by delivering unmatched gaming experiences and innovative promotions. 2. High Player Churn:Combat churn with data-driven marketing and personalized offerings to boost player lifetime value. 3. ROI Challenges:Optimize your mix of games and services to balance player satisfaction and profitability. The Road Ahead: Commitment to Digital Transformation With the global online gaming market projected to grow at a significant pace, casinos must embrace a future grounded in digital and data-driven marketing. Investments in technology, analytics, and talent will be pivotal in securing long-term profitability and differentiation. In an industry where chance often rules, success lies in a deliberate, strategic approach to digital marketing. This insight equips you with the tools to not only compete but thrive in this dynamic landscape. Contact Tectonic today to explore Salesforce tools to better reach, engage, and serve your guests. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
Meta Joins the Race to Reinvent Search with AI

Meta Joins the Race to Reinvent Search with AI

Meta Joins the Race to Reinvent Search with AI Meta, the parent company of Facebook, Instagram, and WhatsApp, is stepping into the evolving AI-driven search landscape. As vendors increasingly embrace generative AI to transform search experiences, Meta aims to challenge Google’s dominance in this space. The company is reportedly developing an AI-powered search engine designed to provide conversational, AI-generated summaries of recent events and news. These summaries would be delivered via Meta’s AI chatbot, supported by a multiyear partnership with Reuters for real-time news insights, according to The Information. AI Search: A Growing Opportunity The push comes as generative AI reshapes search technology across the industry. Google, the long-standing leader, has integrated AI features such as AI Overviews into its search platform, offering users summarized search results, product comparisons, and more. This feature, now available in over 100 countries as of October 2024, signals a shift in traditional search strategies. Similarly, OpenAI, the creator of ChatGPT, has been exploring its own AI search model, SearchGPT, and forging partnerships with media organizations like the Associated Press and Hearst. However, OpenAI faces legal challenges, such as a lawsuit from The New York Times over alleged copyright infringement. Meta’s entry into AI-powered search aligns with a broader trend among tech giants. “It makes sense for Meta to explore this,” said Mark Beccue, an analyst with TechTarget’s Enterprise Strategy Group. He noted that Meta’s approach seems more targeted at consumer engagement than enterprise solutions, particularly appealing to younger audiences who are shifting away from traditional search behaviors. Shifting User Preferences Generational changes in search habits are creating opportunities for new players in the market. Younger users, particularly Gen Z and Gen Alpha, are increasingly turning to platforms like TikTok for lifestyle advice and Amazon for product recommendations, bypassing traditional search engines like Google. “Recent studies show younger generations are no longer using ‘Google’ as a verb,” said Lisa Martin, an analyst with the Futurum Group. “This opens the playing field for competitors like Meta and OpenAI.” Forrester Research corroborates this trend, noting a diversification in search behaviors. “ChatGPT’s popularity has accelerated this shift,” said Nikhil Lai, a Forrester analyst. He added that these changes could challenge Google’s search ad market, with its dominance potentially waning in the years ahead. Meta’s AI Search Potential Meta’s foray into AI search offers an opportunity to enhance user experiences and deepen engagement. Rather than pushing news content into users’ feeds—an approach that has drawn criticism—AI-driven search could empower users to decide what content they see and when they see it. “If implemented thoughtfully, it could transform the user experience and give users more control,” said Martin. This approach could also boost engagement by keeping users within Meta’s ecosystem. The Race for Revenue and Trust While AI-powered search is expected to increase engagement, monetization strategies remain uncertain. Google has yet to monetize its AI Overviews, and OpenAI’s plans for SearchGPT remain unclear. Other vendors, like Perplexity AI, are experimenting with models such as sponsored questions instead of traditional results. Trust remains a critical factor in the evolving search landscape. “Google is still seen as more trustworthy,” Lai noted, with users often returning to Google to verify AI-generated information. Despite the competition, the conversational AI search market lacks a definitive leader. “Google dominated traditional search, but the race for conversational search is far more open-ended,” Lai concluded. Meta’s entry into this competitive space underscores the ongoing evolution of search technology, setting the stage for a reshaped digital landscape driven by AI innovation. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
salesforce government digital transformation

Salesforce Drives Digital Transformation in Governmental Agencies

How Salesforce Drives Digital Transformation in Governmental Agencies in 2025 In the evolving digital age, government agencies face an increasing demand to modernize their services, improve citizen engagement, and deliver seamless digital experiences. These organizations require transformational technologies that not only streamline internal operations but also adopt a citizen-first approach. Salesforce emerges as a key enabler of this transformation, empowering government agencies with tools to build unified, transparent platforms while fostering efficiency and enhancing citizen interaction. Leveraging Salesforce Commerce Cloud and Salesforce CRM, agencies can overcome common challenges and embrace a more digitally enabled public sector. Let’s explore the pressing challenges government agencies face and how Salesforce provides practical, scalable solutions to address them. 1. Citizen Engagement and Accessibility: Bridging the Digital Divide Challenge: Citizens now expect government services to be as user-friendly and accessible as private-sector experiences. Lengthy response times, disconnected platforms, and inconsistent experiences across digital and physical touchpoints erode trust and hinder accessibility. Solution: 2. Data Security and Compliance: Safeguarding Citizen Trust Challenge: Handling sensitive citizen data requires robust security and strict compliance with regulations like GDPR, CCPA, and other local data privacy laws. Solution: 3. Legacy Systems and Integration: Modernizing Infrastructure Challenge: Legacy systems often limit agility, making it difficult to integrate new technologies and slowing the pace of digital transformation. Solution: 4. Budget Constraints: Implementing Cost-Effective Solutions Challenge: Budget limitations often hinder the adoption of new technologies, especially those requiring significant upfront investment. Solution: 5. Efficient Service Delivery: Streamlining Workflows Challenge: Paper-heavy, bureaucratic processes delay service delivery and frustrate both staff and citizens. Solution: 6. Data-Driven Decision-Making: Analytics for Informed Policies Challenge: Generating actionable insights from vast amounts of data is challenging, affecting policymaking and government efficiency. Solution: 7. Enhancing Collaboration: A Unified Workforce Challenge: Siloed departments hinder collaboration and reduce overall productivity, making it difficult to provide cohesive citizen services. Solution: 8. Real-Time Responsiveness: Meeting Citizen Expectations Challenge: Citizens expect real-time support and proactive communication from government agencies. Delays lead to frustration and diminished trust. Solution: Transforming Government Services with Salesforce Salesforce Commerce Cloud and Salesforce CRM are tailored to address public sector challenges in 2025. By leveraging these tools, government agencies can: Salesforce offers a clear path to a digitally empowered future, enabling government agencies to meet today’s demands while laying the foundation for innovation. Ready to Transform?If your agency is ready to embrace digital transformation, streamline operations, and enhance citizen services, Salesforce can help you get there. Let’s discuss how Salesforce solutions, supported by expert implementation, can drive meaningful change for your organization and your citizens. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
AI Energy Solution

AI Energy Solution

Could the AI Energy Solution Make AI Unstoppable? The Rise of Brain-Based AI In 2002, Jason Padgett, a furniture salesman from Tacoma, Washington, experienced a life-altering transformation after a traumatic brain injury. Following a violent assault, Padgett began to perceive the world through intricate patterns of geometry and fractals, developing a profound, intuitive grasp of advanced mathematical concepts—despite no formal education in the subject. His extraordinary abilities, emerging from the brain’s adaptation to injury, revealed an essential truth: the human brain’s remarkable capacity for resilience and reorganization. This phenomenon underscores the brain’s reliance on inhibition, a critical mechanism that silences or separates neural processes to conserve energy, clarify signals, and enable complex cognition. Researcher Iain McGilchrist highlights that this ability to step back from immediate stimuli fosters reflection and thoughtful action. Yet this foundational trait—key to the brain’s efficiency and adaptability—is absent from today’s dominant AI models. Current AI systems, like Transformers powering tools such as ChatGPT, lack inhibition. These models rely on probabilistic predictions derived from massive datasets, resulting in inefficiencies and an inability to learn independently. However, the rise of brain-based AI seeks to emulate aspects of inhibition, creating systems that are not only more energy-efficient but also capable of learning from real-world, primary data without constant retraining. The AI Energy Problem Today’s AI landscape is dominated by Transformer models, known for their ability to process vast amounts of secondary data, such as scraped text, images, and videos. While these models have propelled significant advancements, their insatiable demand for computational power has exposed critical flaws. As energy costs rise and infrastructure investment balloons, the industry is beginning to reevaluate its reliance on Transformer models. This shift has sparked interest in brain-inspired AI, which promises sustainable solutions through decentralized, self-learning systems that mimic human cognitive efficiency. What Brain-Based AI Solves Brain-inspired models aim to address three fundamental challenges with current AI systems: The human brain’s ability to build cohesive perceptions from fragmented inputs—like stitching together a clear visual image from saccades and peripheral signals—serves as a blueprint for these models, demonstrating how advanced functionality can emerge from minimal energy expenditure. The Secret to Brain Efficiency: A Thousand Brains Jeff Hawkins, the creator of the Palm Pilot, has dedicated decades to understanding the brain’s neocortex and its potential for AI design. His Thousand Brains Theory of Intelligence posits that the neocortex operates through a universal algorithm, with approximately 150,000 cortical columns functioning as independent processors. These columns identify patterns, sequences, and spatial representations, collaborating to form a cohesive perception of the world. Hawkins’ brain-inspired approach challenges traditional AI paradigms by emphasizing predictive coding and distributed processing, reducing energy demands while enabling real-time learning. Unlike Transformers, which centralize control, brain-based AI uses localized decision-making, creating a more scalable and adaptive system. Is AI in a Bubble? Despite immense investment in AI, the market’s focus remains heavily skewed toward infrastructure rather than applications. NVIDIA’s data centers alone generate 5 billion in annualized revenue, while major AI applications collectively bring in just billion. This imbalance has led to concerns about an AI bubble, reminiscent of the early 2000s dot-com and telecom busts, where overinvestment in infrastructure outpaced actual demand. The sustainability of current AI investments hinges on the viability of new models like brain-based AI. If these systems gain widespread adoption within the next decade, today’s energy-intensive Transformer models may become obsolete, signaling a profound market correction. Controlling Brain-Based AI: A Philosophical Divide The rise of brain-based AI introduces not only technical challenges but also philosophical ones. Scholars like Joscha Bach argue for a reductionist approach, constructing intelligence through mathematical models that approximate complex phenomena. Others advocate for holistic designs, warning that purely rational systems may lack the broader perspective needed to navigate ethical and unpredictable scenarios. This philosophical debate mirrors the physical divide in the human brain: one hemisphere excels in reductionist analysis, while the other integrates holistic perspectives. As AI systems grow increasingly complex, the philosophical framework guiding their development will profoundly shape their behavior—and their impact on society. The future of AI lies in balancing efficiency, adaptability, and ethical design. Whether brain-based models succeed in replacing Transformers will depend not only on their technical advantages but also on our ability to guide their evolution responsibly. As AI inches closer to mimicking human intelligence, the stakes have never been higher. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

Read More
DHS Introduces AI Framework to Protect Critical Infrastructure

DHS Introduces AI Framework to Protect Critical Infrastructure

The Department of Homeland Security (DHS) has unveiled the Roles and Responsibilities Framework for Artificial Intelligence in Critical Infrastructure, a voluntary set of guidelines designed to ensure the safe and secure deployment of AI across the systems that power daily life. From energy grids to water systems, transportation, and communications, critical infrastructure increasingly relies on AI for enhanced efficiency and resilience. While AI offers transformative potential—such as detecting earthquakes, optimizing energy usage, and streamlining logistics—it also introduces new vulnerabilities. Framework Overview The framework, developed with input from cloud providers, AI developers, critical infrastructure operators, civil society, and public sector organizations, builds on DHS’s broader policies from 2023, which align with White House directives. It aims to provide a shared roadmap for balancing AI’s benefits with its risks. AI Vulnerabilities in Critical Infrastructure The DHS framework categorizes vulnerabilities into three key areas: The guidelines also address sector-specific vulnerabilities and offer strategies to ensure AI strengthens resilience while minimizing misuse risks. Industry and Government Support Arvind Krishna, Chairman and CEO of IBM, lauded the framework as a “powerful tool” for fostering responsible AI development. “We look forward to working with DHS to promote shared and individual responsibilities in advancing trusted AI systems.” Marc Benioff, CEO of Salesforce, emphasized the framework’s role in fostering collaboration among stakeholders while prioritizing trust and accountability. “Salesforce is committed to humans and AI working together to advance critical infrastructure industries in the U.S. We support this framework as a vital step toward shaping the future of AI in a safe and sustainable manner.” DHS Secretary Alejandro N. Mayorkas highlighted the urgency of proactive action. “AI offers a once-in-a-generation opportunity to improve the strength and resilience of U.S. critical infrastructure, and we must seize it while minimizing its potential harms. The framework, if widely adopted, will help ensure the safety and security of critical services.” DHS Recommendations for Stakeholders A Call to Action DHS encourages widespread adoption of the framework to build safer, more resilient critical infrastructure. By prioritizing trust, transparency, and collaboration, this initiative aims to guide the responsible integration of AI into essential systems, ensuring they remain secure and effective as technology continues to evolve. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
AI Won't Hurt Salesforce

AI Won’t Hurt Salesforce

Marc Benioff Dismisses AI Threats, Sets Sights on a Billion AI Agents in One Year Salesforce CEO Marc Benioff has no doubts about the transformative potential of AI for enterprise software, particularly Salesforce itself. At the core of his vision are AI agents—autonomous software bots designed to handle routine tasks, freeing up human workers to focus on more strategic priorities. “What if your workforce had no limits? That’s a question we couldn’t even ask over the past 25 years of Salesforce—or the 45 years I’ve been in software,” Benioff said during an appearance on TechCrunch’s Equity podcast. The Billion-Agent Goal Benioff revealed that Salesforce’s recently launched Agentforce platform is already being adopted by “hundreds of customers” and aims to deploy a billion AI agents within a year. These agents are designed to handle tasks across industries—from enhancing customer experiences at retail brands like Gucci to assisting patients with follow-ups in healthcare. To illustrate, Benioff shared his experience with Disney’s virtual Private Tour Guides. “The AI agent analyzed park flow, ride history, and preferences, then guided me to attractions I hadn’t visited before,” he explained. Competition with Microsoft and the AI Landscape While Benioff is bullish on AI, he hasn’t hesitated to criticize competitors—particularly Microsoft. When Microsoft unveiled its new autonomous agents for Dynamics 365 in October, Benioff dismissed them as uninspired. “Copilot is the new Clippy,” he quipped, referencing Microsoft’s infamous virtual assistant from the 1990s. Benioff also cited Gartner research highlighting data security issues and administrative flaws in Microsoft’s AI tools, adding, “Copilot has disappointed so many customers. It’s not transforming companies.” However, industry skeptics argue that the real challenge to Salesforce isn’t Microsoft but the wave of AI-powered startups disrupting traditional enterprise software. With tools like OpenAI’s ChatGPT and Klarna’s in-house AI assistant “Kiki,” companies are starting to explore GenAI solutions that can replace legacy platforms like Salesforce altogether. For example, Klarna recently announced it was moving away from Salesforce and Workday, favoring GenAI tools that enable seamless, conversational interfaces and faster data access. Why Salesforce Is Positioned to Win Despite the noise, Benioff remains confident that Salesforce’s extensive data infrastructure gives it a significant edge. “We manage 230 petabytes of customer data with robust security and sharing models. That’s what allows AI to thrive in our ecosystem,” he said. While companies may question how other platforms like OpenAI handle data, Salesforce offers an integrated approach, reducing the need for complex data migrations to other clouds, such as Microsoft Azure. Salesforce’s Own Use of AI Benioff also highlighted Salesforce’s internal adoption of Agentforce, using AI agents in its customer service operations, sales processes, and help centers. “If you’re authenticated on help.salesforce.com, you’re already interacting with our agent,” he noted. AI Startups: Threat or Opportunity? As for concerns about AI startups overtaking Salesforce, Benioff sees them as acquisition opportunities rather than existential threats. “We’ve made over 60 acquisitions, many of them startups,” he said. He pointed to Agentforce itself, which was built using technology from Airkit.ai, a startup founded by a former Salesforce employee. Salesforce Ventures initially invested in Airkit.ai before acquiring and integrating it into its platform. The Path Forward Benioff is resolute in his belief that AI won’t hurt Salesforce—instead, it will revolutionize how businesses operate. While skeptics warn of a seismic shift in enterprise software, Benioff’s strategy is clear: lean into AI, leverage data, and stay agile through innovation and acquisitions. “We’re just getting started,” he concluded, reiterating his vision for a future where AI agents expand the possibilities of work and customer experience like never before. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
healthcare Can prioritize ai governance

Healthcare Can Prioritize AI Governance

As artificial intelligence gains momentum in healthcare, it’s critical for health systems and related stakeholders to develop robust AI governance programs. AI’s potential to address challenges in administration, operations, and clinical care is drawing interest across the sector. As this technology evolves, the range of applications in healthcare will only broaden.

Read More
healthcare Can prioritize ai governance

AI Data Privacy and Security

Three Key Generative AI Data Privacy and Security Concerns The rise of generative AI is reshaping the digital landscape, introducing powerful tools like ChatGPT and Microsoft Copilot into the hands of professionals, students, and casual users alike. From creating AI-generated art to summarizing complex texts, generative AI (GenAI) is transforming workflows and sparking innovation. However, for information security and privacy professionals, this rapid proliferation also brings significant challenges in data governance and protection. Below are three critical data privacy and security concerns tied to generative AI: 1. Who Owns the Data? Data ownership is a contentious issue in the age of generative AI. In the European Union, the General Data Protection Regulation (GDPR) asserts that individuals own their personal data. In contrast, data ownership laws in the United States are less clear-cut, with recent state-level regulations echoing GDPR’s principles but failing to resolve ambiguity. Generative AI often ingests vast amounts of data, much of which may not belong to the person uploading it. This creates legal risks for both users and AI model providers, especially when third-party data is involved. Cases surrounding intellectual property, such as controversies involving Slack, Reddit, and LinkedIn, highlight public resistance to having personal data used for AI training. As lawsuits in this arena emerge, prior intellectual property rulings could shape the legal landscape for generative AI. 2. What Data Can Be Derived from LLM Output? Generative AI models are designed to be helpful, but they can inadvertently expose sensitive or proprietary information submitted during training. This risk has made many wary of uploading critical data into AI models. Techniques like tokenization, anonymization, and pseudonymization can reduce these risks by obscuring sensitive data before it is fed into AI systems. However, these practices may compromise the model’s performance by limiting the quality and specificity of the training data. Advocates for GenAI stress that high-quality, accurate data is essential to achieving the best results, which adds to the complexity of balancing privacy with performance. 3. Can the Output Be Trusted? The phenomenon of “hallucinations” — when generative AI produces incorrect or fabricated information — poses another significant concern. Whether these errors stem from poor training, flawed data, or malicious intent, they raise questions about the reliability of GenAI outputs. The impact of hallucinations varies depending on the context. While some errors may cause minor inconveniences, others could have serious or even dangerous consequences, particularly in sensitive domains like healthcare or legal advisory. As generative AI continues to evolve, ensuring the accuracy and integrity of its outputs will remain a top priority. The Generative AI Data Governance Imperative Generative AI’s transformative power lies in its ability to leverage vast amounts of information. For information security, data privacy, and governance professionals, this means grappling with key questions, such as: With high stakes and no way to reverse intellectual property violations, the need for robust data governance frameworks is urgent. As society navigates this transformative era, balancing innovation with responsibility will determine whether generative AI becomes a tool for progress or a source of new challenges. While generative AI heralds a bold future, history reminds us that groundbreaking advancements often come with growing pains. It is the responsibility of stakeholders to anticipate and address these challenges to ensure a safer and more equitable AI-powered world. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

Read More
Python Alongside Salesforce

Python Losing the Crown

For years, Python has been synonymous with data science, thanks to its robust libraries like NumPy, Pandas, and scikit-learn. It’s long held the crown as the dominant programming language in the field. However, even the strongest kingdoms face threats. Python Losing the Crown. The whispers are growing louder: Is Python’s reign nearing its end? Before you fire up your Jupyter notebook to prove me wrong, let me clarify — Python is incredible and undeniably one of the greatest programming languages of all time. But no ruler is without flaws, and Python’s supremacy may not last forever. Here are five reasons why Python’s crown might be slipping. 1. Performance Bottlenecks: Python’s Achilles’ Heel Let’s address the obvious: Python is slow. Its interpreted nature makes it inherently less efficient than compiled languages like C++ or Java. Sure, libraries like NumPy and tools like Cython help mitigate these issues, but at its core, Python can’t match the raw speed of newer, more performance-oriented languages. Enter Julia and Rust, which are optimized for numerical computing and high-performance tasks. When working with massive, real-time datasets, Python’s performance bottlenecks become harder to ignore, prompting some developers to offload critical tasks to faster alternatives. 2. Python’s Memory Challenges Memory consumption is another area where Python struggles. Handling large datasets often pushes Python to its limits, especially in environments with constrained resources, such as edge computing or IoT. While tools like Dask can help manage memory more efficiently, these are often stopgap solutions rather than true fixes. Languages like Rust are gaining traction for their superior memory management, making them an attractive alternative for resource-limited scenarios. Picture running a Python-based machine learning model on a Raspberry Pi, only to have it crash due to memory overload. Frustrating, isn’t it? 3. The Rise of Domain-Specific Languages (DSLs) Python’s versatility has been both its strength and its weakness. As industries mature, many are turning to domain-specific languages tailored to their specific needs: Python may be the “jack of all trades,” but as the saying goes, it risks being the “master of none” compared to these specialized tools. 4. Python’s Simplicity: A Double-Edged Sword Python’s beginner-friendly syntax is one of its greatest strengths, but it can also create complacency. Its ease of use often means developers don’t delve into the deeper mechanics of algorithms or computing. Meanwhile, languages like Julia, designed for scientific computing, offer intuitive structures for advanced modeling while encouraging developers to engage with complex mathematical concepts. Python’s simplicity is like riding a bike with training wheels: it works, but it may not push you to grow as a developer. 5. AI-Specific Frameworks Are Gaining Ground Python has been the go-to language for AI, powering frameworks like TensorFlow, PyTorch, and Keras. But new challengers are emerging: As AI and machine learning evolve, these specialized frameworks could chip away at Python’s dominance. The Verdict: Python Losing the Crown? Python remains the Swiss Army knife of programming languages, especially in data science. However, its cracks are showing as new, specialized tools and faster languages emerge. The data science landscape is evolving, and Python must adapt or risk losing its crown. For now, Python is still king. But as history has shown, no throne is secure forever. The future belongs to those who innovate, and Python’s ability to evolve will determine whether it remains at the top. The throne of code is only as stable as the next breakthrough. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

Read More
AI Risk Management

AI Risk Management

Organizations must acknowledge the risks associated with implementing AI systems to use the technology ethically and minimize liability. Throughout history, companies have had to manage the risks associated with adopting new technologies, and AI is no exception. Some AI risks are similar to those encountered when deploying any new technology or tool, such as poor strategic alignment with business goals, a lack of necessary skills to support initiatives, and failure to secure buy-in across the organization. For these challenges, executives should rely on best practices that have guided the successful adoption of other technologies. In the case of AI, this includes: However, AI introduces unique risks that must be addressed head-on. Here are 15 areas of concern that can arise as organizations implement and use AI technologies in the enterprise: Managing AI Risks While AI risks cannot be eliminated, they can be managed. Organizations must first recognize and understand these risks and then implement policies to minimize their negative impact. These policies should ensure the use of high-quality data, require testing and validation to eliminate biases, and mandate ongoing monitoring to identify and address unexpected consequences. Furthermore, ethical considerations should be embedded in AI systems, with frameworks in place to ensure AI produces transparent, fair, and unbiased results. Human oversight is essential to confirm these systems meet established standards. For successful risk management, the involvement of the board and the C-suite is crucial. As noted, “This is not just an IT problem, so all executives need to get involved in this.” Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

Read More
NYT Issues Cease-and-Desist Letter to Perplexity AI

NYT Issues Cease-and-Desist Letter to Perplexity AI

NYT Issues Cease-and-Desist Letter to Perplexity AI Over Alleged Unauthorized Content Use The New York Times (NYT) has issued a cease-and-desist letter to Perplexity AI, accusing the AI-powered search startup of using its content without permission. This move marks the second time the NYT has confronted a company for allegedly misappropriating its material. According to reports, the Times claims Perplexity is accessing and utilizing its content to generate summaries and other outputs, actions it argues infringe on copyright laws. The startup now has two weeks to respond to the accusations. A Growing Pattern of Tensions Perplexity AI is not the only publisher-facing scrutiny. In June, Forbes threatened legal action against the company, alleging “willful infringement” by using its text and images. In response, Perplexity launched the Perplexity Publishers’ Program, a revenue-sharing initiative that collaborates with publishers like Time, Fortune, and The Texas Tribune. Meanwhile, the NYT remains entangled in a separate lawsuit with OpenAI and its partner Microsoft over alleged misuse of its content. A Strategic Legal Approach The NYT’s decision to issue a cease-and-desist letter instead of pursuing an immediate lawsuit signals a calculated move. “Cease-and-desist approaches are less confrontational, less expensive, and faster,” said Sarah Kreps, a professor at Cornell University. This method also opens the door for negotiation, a pragmatic step given the uncharted legal terrain surrounding generative AI and copyright law. Michael Bennett, a responsible AI expert from Northeastern University, echoed this view, suggesting that the cease-and-desist approach positions the Times to protect its intellectual property while maintaining leverage in ongoing legal battles. If the NYT wins its case against OpenAI, Bennett added, it could compel companies like Perplexity to enter financial agreements for content use. However, if the case doesn’t favor the NYT, the publisher risks losing leverage. The letter also serves as a warning to other AI vendors, signaling the NYT’s determination to safeguard its intellectual property. Perplexity’s Defense: Facts vs. Expression Perplexity AI has countered the NYT’s claims by asserting that its methods adhere to copyright laws. “We aren’t scraping data for building foundation models but rather indexing web pages and surfacing factual content as citations,” the company stated. It emphasized that facts themselves cannot be copyrighted, drawing parallels to how search engines like Google operate. Kreps noted that Perplexity’s approach aligns closely with other AI platforms, which typically index pages to provide factual answers while citing sources. “If Perplexity is culpable, then the entire AI industry could be held accountable,” she said, contrasting Perplexity’s citation-based model with platforms like ChatGPT, which often lack transparency about data sources. The Crux of the Copyright Argument The NYT’s cease-and-desist letter centers on the distinction between facts and the creative expression of facts. While raw facts are not protected under copyright, the NYT claims that its specific interpretation and presentation of those facts are. Vincent Allen, an intellectual property attorney, explained that if Perplexity is scraping data and summarizing articles, it may involve making unauthorized copies of copyrighted content, strengthening the NYT’s claims. “This is a big deal for content providers,” Allen said, “as they want to ensure they’re compensated for their work.” Implications for the AI Industry The outcome of this dispute could set a precedent for how AI platforms handle content generated by publishers. If Perplexity’s practices are deemed infringing, it could reshape the operational models of similar AI vendors. At the heart of the debate is the balance between fostering innovation in AI and protecting intellectual property, a challenge that will likely shape the future of generative AI and its relationship with content creators. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
  • 1
  • 2
gettectonic.com